1. Field of the Invention
The present invention relates to an information processing apparatus, an information processing method, and a program, and more particularly, to an information processing apparatus, an information processing method, and a program which can generate time series data more correctly.
2. Description of the Related Art
The present applicant has suggested an invention of generating time series data according to the result of learning using recurrent neural networks (for example, refer to Jpn. Pat. Appln. Laid-Open Publication No. 11-126198).
According to this suggestion, as shown in FIG. 1, an information processing apparatus basically includes a network of lower hierarchy having recurrent neural networks (referred to as RNNs, hereinafter) 1-1 to 1-v, and a network of upper hierarchy having RNNs 11-1 to 11-v.
In the lower hierarchy network, outputs from the RNNs 1-1 to 1-v are supplied to a composition circuit 3 through corresponding gates 2-1 to 2-v to be composited.
Similarly, in the upper hierarchy network, outputs from the RNNs 11-1 to 11-v are supplied to a composition circuit 13 through corresponding gates 12-1 to 12-v to be composited. Then, based on the output from the composition circuit 13 of the upper hierarchy network, the on/off of the lower hierarchy gates 2-1 to 2-v is controlled.
In the information processing apparatus shown in FIG. 1, the lower hierarchy RNNs 1-1 to 1-v are made to generate time series data P1 to Pv respectively, and a predetermined gate of the lower hierarchy gates 2-1 to 2-v is set on or set off based on the output from the upper hierarchy composition circuit 13. Thus, one of the time series data P1 to Pv, which is output from a predetermined one of the RNNs 1-1 to 1-v, can be selectively output from the composition circuit 3.
Accordingly, for example, as shown in FIG. 2, time series data can be generated such that the time series data P1 is generated for a predetermined time period, and the time series data P2 is generated for a next predetermined time period, and then the time series data P1 is generated for a next predetermined time period again.